Reports of the AAAI 2019 spring symposium series

Ioana Baldini; Clark Barrett; Antonio Chella; Carlos Cinelli; David Gamez; Leilani H. Gilpin; Knut Hinkelmann; Dylan Holmes; Takashi Kido; Murat Kocaoglu; William F. Lawless; Alessio Lomuscio; Jamie C. Macbeth; Andreas Martin; Ranjeev Mittu; Evan Patterson; Donald Sofge; Prasad Tadepalli; Keiki Takadama; Shomir Wilson

doi:10.1609/aimag.v40i3.5181

Reports of the AAAI 2019 spring symposium series

Ioana Baldini, Clark Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson

College of Information Sciences and Technology

Research output: Contribution to journal › Article › peer-review

Abstract

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates.

Original language	English (US)
Pages (from-to)	59-66
Number of pages	8
Journal	AI Magazine
Volume	40
Issue number	3
DOIs	https://doi.org/10.1609/aimag.v40i3.5181
State	Published - 2019

All Science Journal Classification (ASJC) codes

Artificial Intelligence

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Baldini, I., Barrett, C., Chella, A., Cinelli, C., Gamez, D., Gilpin, L. H., Hinkelmann, K., Holmes, D., Kido, T., Kocaoglu, M., Lawless, W. F., Lomuscio, A., Macbeth, J. C., Martin, A., Mittu, R., Patterson, E., Sofge, D., Tadepalli, P., Takadama, K., & Wilson, S. (2019). Reports of the AAAI 2019 spring symposium series. AI Magazine, 40(3), 59-66. https://doi.org/10.1609/aimag.v40i3.5181

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abstract = "Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must {"}build machines that make sense of what goes on in their environment,{"} a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates.",

author = "Ioana Baldini and Clark Barrett and Antonio Chella and Carlos Cinelli and David Gamez and Gilpin, {Leilani H.} and Knut Hinkelmann and Dylan Holmes and Takashi Kido and Murat Kocaoglu and Lawless, {William F.} and Alessio Lomuscio and Macbeth, {Jamie C.} and Andreas Martin and Ranjeev Mittu and Evan Patterson and Donald Sofge and Prasad Tadepalli and Keiki Takadama and Shomir Wilson",

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year = "2019",

doi = "10.1609/aimag.v40i3.5181",

language = "English (US)",

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pages = "59--66",

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Baldini, I, Barrett, C, Chella, A, Cinelli, C, Gamez, D, Gilpin, LH, Hinkelmann, K, Holmes, D, Kido, T, Kocaoglu, M, Lawless, WF, Lomuscio, A, Macbeth, JC, Martin, A, Mittu, R, Patterson, E, Sofge, D, Tadepalli, P, Takadama, K & Wilson, S 2019, 'Reports of the AAAI 2019 spring symposium series', AI Magazine, vol. 40, no. 3, pp. 59-66. https://doi.org/10.1609/aimag.v40i3.5181

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AU - Baldini, Ioana

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AU - Chella, Antonio

AU - Cinelli, Carlos

AU - Gamez, David

AU - Gilpin, Leilani H.

AU - Hinkelmann, Knut

AU - Holmes, Dylan

AU - Kido, Takashi

AU - Kocaoglu, Murat

AU - Lawless, William F.

AU - Lomuscio, Alessio

AU - Macbeth, Jamie C.

AU - Martin, Andreas

AU - Mittu, Ranjeev

AU - Patterson, Evan

AU - Sofge, Donald

AU - Tadepalli, Prasad

AU - Takadama, Keiki

AU - Wilson, Shomir

PY - 2019

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AB - Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates.

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Reports of the AAAI 2019 spring symposium series

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